IMPORTANCEThe National COVID Cohort Collaborative (N3C) is a centralized, harmonized, highgranularity electronic health record repository that is the largest, most representative COVID-19 cohort to date. This multicenter data set can support robust evidence-based development of predictive and diagnostic tools and inform clinical care and policy.OBJECTIVES To evaluate COVID-19 severity and risk factors over time and assess the use of machine learning to predict clinical severity. DESIGN, SETTING, AND PARTICIPANTSIn a retrospective cohort study of 1 926 526 US adults with SARS-CoV-2 infection (polymerase chain reaction >99% or antigen <1%) and adult patients without SARS-CoV-2 infection who served as controls from 34 medical centers nationwide between January 1, 2020, and December 7, 2020, patients were stratified using a World Health Organization COVID-19 severity scale and demographic characteristics. Differences between groups over time were evaluated using multivariable logistic regression. Random forest and XGBoost models were used to predict severe clinical course (death, discharge to hospice, invasive ventilatory support, or extracorporeal membrane oxygenation). MAIN OUTCOMES AND MEASURESPatient demographic characteristics and COVID-19 severity using the World Health Organization COVID-19 severity scale and differences between groups over time using multivariable logistic regression. RESULTSThe cohort included 174 568 adults who tested positive for SARS-CoV-2 (mean [SD] age, 44.4 [18.6] years; 53.2% female) and 1 133 848 adult controls who tested negative for SARS-CoV-2 (mean [SD] age, 49.5 [19.2] years; 57.1% female). Of the 174 568 adults with SARS-CoV-2, 32 472(18.6%) were hospitalized, and 6565 (20.2%) of those had a severe clinical course (invasive ventilatory support, extracorporeal membrane oxygenation, death, or discharge to hospice). Of the hospitalized patients, mortality was 11.6% overall and decreased from 16.4% in March to April 2020 to 8.6% in September to October 2020 (P = .002 for monthly trend). Using 64 inputs available on the first hospital day, this study predicted a severe clinical course using random forest and XGBoost models (area under the receiver operating curve = 0.87 for both) that were stable over time. The factor most strongly associated with clinical severity was pH; this result was consistent across machine learning methods. In a separate multivariable logistic regression model built for inference, (continued) Key Points Question In a US data resource large enough to adjust for multiple confounders, what risk factors are associated with COVID-19 severity and severity trajectory over time, and can machine learning models predict clinical severity? Findings In this cohort study of 174 568 adults with SARS-CoV-2, 32 472 (18.6%) were hospitalized and 6565 (20.2%) were severely ill, and first-day machine learning models accurately predicted clinical severity. Mortality was 11.6%

Childhood obesity is a global epidemic. Health video games are an emerging intervention strategy to combat childhood obesity. This systematic review examined published research on the effect of health video games on childhood obesity. Fourteen articles examining 28 health video games published between 2005 and 2013 in English were selected from 2,433 articles identified through five major search engines. Results indicated that academic interest in using health video games for childhood obesity prevention has increased during this time. Most games were commercially available. Most studies were of short duration. Diverse player and game play patterns have been identified. Most studies involved players of both genders with slightly more boys. The majority of players were non-Caucasian. Most studies had the players play the games at home, while some extended the play setting to school and sports/recreational facilities. Most of the games were commercially available. Positive outcomes related to obesity were observed in about 40% of the studies, all of which targeted overweight or obese participants.

Objective This paper presents the current state of patient-reported outcome measures, and explains new opportunities for leveraging the recent adoption of electronic health records to expand the application of patient-reported outcomes in both clinical care and comparative effectiveness research. Study Design and Setting Historic developments of patient-reported outcome, electronic health record, and comparative effectiveness research are analyzed in two dimensions: patient-centeredness and digitization. We pose the question: “What needs to be standardized around the collection of patient-reported outcomes in electronic health records for comparative effectiveness research?” Results We identified three converging trends: the progression of patient-reported outcomes toward greater patient centeredness and electronic adaptation; the evolution of electronic health records into personalized and fully digitized solutions; the shift toward patient-oriented comparative effectiveness research. Related to this convergence, we propose an architecture for patient-reported outcome standardization that could serve as a first step toward a more comprehensive integration of patient-reported outcomes with electronic health record for both practice and research. Conclusion The science of patient-reported outcome measurement has matured sufficiently to be integrated routinely into electronic health records and other e-health solutions to collect data on an ongoing basis for clinical care and comparative effectiveness research. Further efforts and ideally coordinated efforts from various stakeholders are needed to refine the details of the proposed framework for standardization.